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Exploration of three-dimensional structural space has become crucial for the development of novel bioactive molecules. In this context, polar spirocycles have emerged as key scaffolds due to their enhanced 3D character and well-defined spatial orientation. Herein, we report the development of a highly modular strategy to access beta-spirocyclic pyrrolidine derivatives from readily available starting materials, i.e., cyclic ketones and amino or oxamic acids. The sequence proceeds through a straightforward Knoevenagel condensation, followed by a domino Giese-type reaction/base-mediated cyclisation process, delivering a broad scope of polar spirocyclic scaffolds in good to excellent yields. The products can be readily diversified to access a wider range of spirocyclic cores (such as lactams or succinimides), thus increasing the versatility of our method to gain rapid access to libraries of potential drug-like molecules.

Time series forecasting of disaggregated freight flow is a key issue in decision-making by port authorities. For this purpose and to test new deep learning techniques we have selected seven time series of imported goods from Morocco to Spain through the port of Algeciras, and we have tested two forecasting deep neural networks models: dilated causal convolutional and encoder–decoder recurrent. We have experimented with four different granularities for each series: quarterly, monthly, weekly and daily. The results show that our neural network models can manage these raw series without first removing seasonality or trend. We also highlight the ability of neural models to work with a fixed input size of one year, being able to make good predictions using the same input size for all granularities. The two deep learning models have globally improved the benchmarks of the M4 Competition of forecasting. Each neural network model obtains its best results under different circumstances: the recurrent one with daily granularity and intermittent series, and the convolutional one with weekly and monthly granularities.

The purpose of this study is to guide pricing policies of Airbnb accommodation rentals to reduce inefficient pricing strategies through a novel application of topic modelling and a fuzzy clustering. In particular, the method proposes the application of Structural Topic Modelling, which explains a set of observations from latent topics. The associations between topics by Fuzzy C-Means Clustering are analysed to obtain new, more compact representations of topics (i.e., metatopics). This research identifies 15-metatopics related to Airbnb accommodations based on location and connectivity, enjoyment of domestic and everyday services, and the possibility of more authentic local experiences, among others. The influence of key metatopics on the price of Airbnb accommodations is determined by applying Extreme Gradient Boosting (an efficient and scalable implementation of gradient boosting framework) and Shapley Additive Explanations values. To sum up, our research provides an explicit contribution of user-generated content to promote the development of mutually beneficial relationships between guests and hosts, and detects future lines of research and practical and conceptual implications of the findings.

Due to their abundance and readily available synthesis, alcohols provide ideal handles for the selective derivatisation of organic molecules. Radical chemistry offers versatile strategies for the conversion of Csp3–O bonds into a wide range of Csp3–C or Csp3–heteroatom bonds. In these reactions, alcohols are readily derivatised with an activator group which can undergo facile mesolysis to generate a primary, secondary, or tertiary open-shell species that can engage in further transformations. These strategies are particularly effective at overcoming steric limitations associated with nucleophilic substitution pathways. Despite their potential, the use of radical deoxyfunctionalisation reactions as a general strategy for the synthesis of useful and complex molecules remains underutilised. Herein, we highlight recent advancements in this exciting field using photocatalysis, transition metal catalysis or electrochemistry to initiate the radical processes.

The detection of communities in social networks is a task with multiple applications both in research and in sectors such as marketing and politics among others. In this paper, we address the task of detecting on-line communities of Twitter users for a given domain. Our main contribution consists in modelling the community detection problem as a biclustering task. We have performed the experimentation with data from the political domain, a very dynamic area with a large number of interested users and a high availability of tweets. We have evaluated our proposal using both extrinsic and intrinsic methods, reaching very good results in both cases. We use the silhouette coefficient as intrinsic metric for clustering evaluation, and a classification task of political leanings of Twitter users as extrinsic evaluation. One of the most interesting conclusions of our experiments is the quality, from the point of view of predictive capacity in the classification task, of the communities identified with the proposed method. The information provided by communities detected through “follow” relationships has a predictive capacity comparable to that of the contents of tweets written by users. The results also show how detected communities can give insights about future events related to these communities that arise around social networks.

The synthesis of tertiary alkyl fluorides through a formal radical deoxyfluorination process is described herein. This light-mediated, catalyst-free methodology is fast and broadly applicable allowing for the preparation of C-F bonds from (hetero)benzylic, propargylic, and non-activated tertiary alcohol derivatives. Preliminary mechanistic studies support that the key step of the reaction is the single-electron oxidation of cesium oxalates-which are readily available from the corresponding tertiary alcohols-with in situ generated TEDA2+. (TEDA: N-(chloromethyl)triethylenediamine), a radical cation derived from Selectfluor®.

Enterprise Information Systems are enjoying an extensive trajectory in the optimization of organizations worldwide, of which predominantly the Business Process Management (BPM) systems stand out for their great flexibility. BPM models describe business workflows and are highly useful in detecting errors and bottlenecks and in identifying possible improvements. On the other hand, educational management software tools offer a large number of functionalities, but have yet to take advantage of these techniques. Our main objective is to perform an empirical analysis in this unexplored area to evaluate the advantages of applying BPM in the implementation of innovative and dynamic teaching activities. Using this methodology, we have designed RubricaSoft, a BPM system focused on providing dynamic educational processes. It automates multiple tasks, including peer evaluation, information integration and the management of deadlines. The results have been very promising from the point of view of the three axes upon which the evaluation has been carried out: satisfaction of students, improvement in academic results and increase in the productivity of teachers. In one of the processes, the time spent by the teacher has been reduced by 80% and student participation increased by 41%.

Herein we present a highly efficient, light-mediated, deoxygenative protocol to access g-oxo-a-amino acid derivatives.This radical methodology employs photoredox catalysis, in combination with triphenylphosphine, to generate acyl radicals from readily available (hetero)aromatic and vinylic carboxylic acids. This approach allows for the straightforward synthesis of g-oxo-aamino acids bearing a wide range of functional groups (e.g. Cl, CN, furan, thiophene, Bpin) in synthetically useful yields (~ 60% average yield). To further highlight the utility of the methodology, several deprotection and derivatization reactions were carried out.

The synthesis of tertiary alkyl fluorides through a formal radical deoxyfluorination process is described herein. This light‐mediated, catalyst‐free methodology is fast and broadly applicable allowing for the preparation of C−F bonds from (hetero)benzylic, propargylic, and non‐activated tertiary alcohol derivatives. Preliminary mechanistic studies support that the key step of the reaction is the single‐electron oxidation of cesium oxalates—which are readily available from the corresponding tertiary alcohols—with in situ generated TEDA2+. (TEDA: N‐(chloromethyl)triethylenediamine), a radical cation derived from Selectfluor®., C−F bond formation with no need for activation: The synthesis of tertiary alkyl fluorides through a formal radical deoxyfluorination process is described herein. This light‐mediated, catalyst‐free methodology is fast and broadly applicable allowing for the preparation of C−F bonds from (hetero)benzylic, propargylic, and non‐activated tertiary alcohol derivatives. Preliminary investigations support a very efficient radical chain mechanism in which TEDA2+. (TEDA: N‐(chloromethyl)triethylenediamine) is the key species.

Vector-based word representations can help to improve a document classifier.The information of word2vec vectors and bags of words are very complementary.The combination of word2vec and BOW word representations obtains the best results.Word2vec is much more stable than bag of words models in cross-domain experiments. In this paper we show how a vector-based word representation obtained via word2vec can help to improve the results of a document classifier based on bags of words. Both models allow obtaining numeric representations from texts, but they do it very differently. The bag of words model can represent documents by means of widely dispersed vectors in which the indices are words or groups of words. word2vec generates word level representations building vectors that are much more compact, where indices implicitly contain information about the context of word occurrences. Bags of words are very effective for document classification and in our experiments no representation using only word2vec vectors is able to improve their results. However, this does not mean that the information provided by word2vec is not useful for the classification task. When this information is used in combination with the bags of words, the results are improved, showing its complementarity and its contribution to the task. We have also performed cross-domain experiments in which word2vec has shown much more stable behavior than bag of words models.

We explore the idea of integrating both textual and structural information.Using only structural information gives similar results to ones yielded BoW model.Complementing textual content with structural information achieves the best results.A proper combination scheme is critical when integrating both types of models.Experimental results show that our combination proposal is quite effective. Display Omitted Twitter is a worldwide social media platform where millions of people frequently express ideas and opinions about any topic. This widespread success makes the analysis of tweets an interesting and possibly lucrative task, being those tweets rarely objective and becoming the targeting for large-scale analysis. In this paper, we explore the idea of integrating two fundamental aspects of a tweet, the proper textual content and its underlying structural information, when addressing the tweet categorization task. Thus, not only we analyze textual content of tweets but also analyze the structural information provided by the relationship between tweets and users, and we propose different methods for effectively combining both kinds of feature models extracted from the different knowledge sources. In order to test our approach, we address the specific task of determining the political opinion of Twitter users within their political context, observing that our most refined knowledge integration approach performs remarkably better (about 5 points above) than the textual-based classic model.

This paper introduces the potential for reusing UI elements in the context of Model-Based UI Development (MBUID) and provides guidance for future MBUID systems with enhanced reutilization capabilities. Our study is based upon the development of six inter-related projects with a specific MBUID environment which supports standard techniques for reuse such as parametrization and sub-specification, inclusion or shared repositories.We analyze our experience and discuss the benefits and limitations of each technique supported by our MBUID environment. The system architecture, the structure and composition of UI elements and the models specification languages have a decisive impact on reusability. In our case, more than 40% of the elements defined in the UI specifications were reused, resulting in a reduction of 55% of the specification size. Inclusion, parametrization and sub-specification have facilitated modularity and internal reuse of UI specifications at development time, whereas the reuse of UI elements between applications has greatly benefited from sharing repositories of UI elements at run time. HighlightsReusing UI assets reduces the development effort in software industry.We provide a real case study where reusing UI resulted in significant benefits in productivity.The structure of UI Models and its components has a significant impact on reusability.MBUIDEs with run-time architecture can benefit from sharing repositories between various projects.

In this paper, MCFS (Min-Cut-based feature-selection) is presented, which is a feature-selection algorithm based on the representation of the features in a dataset by means of a directed graph. The main contribution of our work is to show the usefulness of a general graph-processing technique in the feature-selection problem for classification datasets. The vertices of the graphs used herein are the features together with two special-purpose vertices (one of which denotes high correlation to the feature class of the dataset, and the other denotes a low correlation to the feature class). The edges are functions of the correlations among the features and also between the features and the classes. A classic max-flow min-cut algorithm is applied to this graph. The cut returned by this algorithm provides the selected features. We have compared the results of our proposal with well-known feature-selection techniques. Our algorithm obtains results statistically similar to those achieved by the other techniques in terms of number of features selected, while additionally significantly improving the accuracy.

An extensible and modular approach for normalizing Spanish tweets is proposed.We make use of lightweight resources build with low manual effort.System performance is also analyzed module-wise and phenomenon-wise.The domain adaptability of our proposed system is easy and successful.The performance increases if a classifier-based reranking process is introduced. Twitter is a social media platform with widespread success where millions of people continuously express ideas and opinions about a myriad of topics. It is a huge and interesting source of data but most of these texts are usually written hastily and very abbreviated, rendering them unsuitable for traditional Natural Language Processing (NLP). The two main contributions of this work are: the characterization of the textual error phenomena in Twitter and the proposal of a modular normalization system that improves the textual quality of tweets. Instead of focusing on a single technique, we propose an extensible normalization system that relies on the combination of several independent "expert modules", each one addressing an very specific error phenomenon in its own way, thus increasing module accuracy and lowering the module building costs. Broadly speaking, the system resembles to an "expert board": modules independently propose correction candidates for each Out of Vocabulary (OOV) word, rank the candidates and the best one is selected. In order to evaluate our proposal, we perform several experiments using texts from Twitter written in Spanish about a specific topic. The flexibility of defining resources at different language levels (core language, domain, genre) combined with the modular architecture lead to lower costs and a good performance: requiring a minimal effort for building the resources and achieving more than 82 % of accuracy compared to the 31 % yielded by the baseline.

Twitter is a social network in which people publish publicly accessible brief, instant messages. With its exponential growth and the public nature and transversality of its contents, more researchers are using Twitter as a source of data for multiple purposes. In this context, the ability to retrieve those messages tweets related to a certain topic becomes critical. In this work, we define the topic-related tweet retrieval task and propose a dynamic, graph-based method with which to address it. We have applied our method to capture a data set containing tweets related to the participation of the Spanish team in the Euro 2012 soccer competition, measuring the precision and recall against other simple but commonly used approaches. The results demonstrate the effectiveness of our method, which significantly increases coverage of the chosen topic and is able to capture related but unknown i priori subtopics.

Nowadays, people do not only navigate the web, but they also contribute contents to the Internet. Among other things, they write their thoughts and opinions in review sites, forums, social networks, blogs and other websites. These opinions constitute a valuable resource for businesses, governments and consumers. In the last years, some researchers have proposed opinion extraction systems, mostly domain-independent ones, to automatically extract structured representations of opinions contained in those texts. In this work, we tackle this task in a domain-oriented approach, defining a set of domain-specific resources which capture valuable knowledge about how people express opinions on a given domain. These resources are automatically induced from a set of annotated documents. Some experiments were carried out on three different domains (user-generated reviews of headphones, hotels and cars), comparing our approach to other state-of-the-art, domain-independent techniques. The results confirm the importance of the domain in order to build accurate opinion extraction systems. Some experiments on the influence of the dataset size and an example of aggregation and visualization of the extracted opinions are also shown.

Trust and Reputation Systems constitute an essential part of many social networks due to the great expansion of these on-line communities in the past few years. As a consequence of this growth, some users try to disturb the normal atmosphere of these communities, or even to take advantage of them in order to obtain some kind of benefits. Therefore, the concept of trust is a key point in the performance of on-line systems such as on-line marketplaces, review aggregators, social news sites, and forums. In this work we propose a method to compute a ranking of the users in a social network, regarding their trustworthiness. The aim of our method is to prevent malicious users from illicitly gaining high reputation in the network by demoting them in the ranking of users. We propose a novel system intended to propagate both positive and negative opinions of the users through a network, in such way that the opinions from each user about others influence their global trust score. Our proposal has been evaluated in different challenging situations. The experiments include the generation of random graphs, the use of a real-world dataset extracted from a social news site, and a combination of both a real dataset and generation techniques, in order to test our proposals in different environments. The results show that our method performs well in every situations, showing the propagation of trust and distrust to be a reliable mechanism in a Trust and Reputation System.

In this paper we present the relevance ranking algorithm named PolarityRank. This algorithm is inspired in PageRank, the webpage relevance calculus method used by Google, and generalizes it to deal with graphs having not only positive but also negative weighted arcs. Besides the definition of our algorithm, this paper includes the algebraic justification, the convergence demonstration and an empirical study in which PolarityRank is applied to two unrelated tasks where a graph with positive and negative weights can be built: the calculation of word semantic orientation and instance selection from a learning dataset.

This paper presents the ideas, experiments and specifications related to the Supervised TextRank (STR) technique, a word tagging method based on the TextRank algorithm. The main innovation of STR technique is the use of a graph-based ranking algorithm similar to PageRank in a supervised fashion, gathering the information needed to build the graph representations of the text from a tagged corpus. We also propose a flexible graph specification language that allows to easily experiment with multiple configurations for the topology of the graph and for the information associated to the nodes and the edges. We have carried experiments in the Part-Of-Speech task, a common tagging problem in Natural Language Processing. In our best result we have achieved a precision of 96.16%, at the same level of the best tagging tools.

In this paper we present InstanceRank, a ranking algorithm that reflects the relevance of the instances within a dataset. InstanceRank applies a similar solution to that used by PageRank, the web pages ranking algorithm in the Google search engine. We also present ISR, an instance selection technique that uses InstanceRank. This algorithm chooses the most representative instances from a learning database. Experiments show that ISR algorithm, with InstanceRank as ranking criteria, obtains similar results in accuracy to other instance reduction techniques, noticeably reducing the size of the instance set.

There are many classification tools that can be used for various NLP tasks, although none of them can be considered the best of all since each one has a particular list of virtues and defects. The combination methods can serve both to maximize the strengths of the base classifiers and to reduce errors caused by their defects improving the results in terms of accuracy. Here is a comparative study on the most relevant methods that shows that combination seems to be a robust and reliable way of improving our results.

Feature-based opinion extraction is a task related to opinion mining and information extraction which consists of automatically extracting feature-level representations of opinions from subjective texts. In the last years, some researchers have proposed domain-independent solutions to this task. Most of them identify the feature being reviewed by a set of words from the text. Rather than that, we propose a domainadaptable opinion extraction system based on feature taxonomies (a semantic representation of the opinable parts and attributes of an object) which extracts feature-level opinions and maps them into the taxonomy. The opinions thus obtained can be easily aggregated for summarization and visualization. In order to increase precision and recall of the extraction system, we define a set of domain-specific resources which capture valuable knowledge about how people express opinions on each feature from the taxonomy for a given domain. These resources are automatically induced from a set of annotated documents. The modular design of our architecture allows building either domain-specific or domainindependent opinion extraction systems. According to some experimental results, using the domain-specific resources leads to far better precision and recall, at the expense of some manual effort.

In this work1, we tackle the problem of spam detection on the Web. Spam web pages have become a problem for Web search engines, due to the negative effects that this phenomenon can cause in their retrieval results. Our approach is based on a random-walk algorithm that obtains a ranking of pages according to their relevance and their spam likelihood. We introduce the novelty of taking into account the content of the web pages to characterize the web graph and to obtain an a priori estimation of the spam likelihood of the web pages. Our graph-based algorithm computes two scores for each node in the graph. Intuitively, these values represent how bad or good (spam-like or not) a web page is, according to its textual content and the relations in the graph. Our experiments show that our proposed technique outperforms other link-based techniques for spam detection. Ministerio de Educación y Ciencia HUM2007-66607-C04-04

Feature-based opinion extraction is a task related to infor- mation extraction, which consists of extracting structured opinions on features of some object from reviews or other subjective textual sources. Over the last years, this prob-lem has been studied by some researchers, generally in an unsupervised, domain-independent manner. As opposed to that, in this work we propose a rede nition of the problem from a more practical point of view, and describe a domain- speci c, resource-based opinion extraction system. We fo-cus on the description and generation of those resources, and brie y report the extraction system architecture and a few initial experiments. The results suggest that domain-speci c knowledge is a valuable resource in order to build precise opinion extraction systems.

In this paper, we provide an overview of the 3rd International Workshop on Search and Mining User-generated Contents, held in conjunction with the 20th ACM International Conference on Information and Knowledge Management. We present the motivation and goals of the workshop, and some statistics and details about accepted papers and keynotes.

The purpose of this study is to compare the movements of people from Andalusia and Catalonia during the years 1995-2006 and to link these movements with social modernization of the same period. The indicators used for this purpose are level of declared income and the years of schooling. The territory has been differed depending on the municipality population size. The findings are summarized in the following ways. An improvement of the overall educational level is observed, except in smaller Andalusian municipalities ≤2000, aged and depopulated, and in bigger >100.000 Andalusian and Catalonian municipalities, repopulated by demographic processes, evident in the Barcelona Metropolitan Region but not in the major cities of Andalusia. Instead, there is a significant relation between population growth and family income in both regions. In Andalusia (for the obseved years) was due to the increase of population and the loss of income was the effect of the loss of population as well. This was not the case in the same way or to the same extent in Catalonia. This formula is unsustainaible, since it is not possible to improved at the expense of indefinite growth. It can be concluded therefore that the territorial homogeneity is an indicator and territorial effect of modernization, but also a condition and a factor for its continuity., El propósito de este trabajo es comparar los movimientos de población de Andalucía y Cataluña durante los años 1995-2006 y relacionar este movimiento con la modernización social del período. Se utilizan como indicadores de modernización el nivel de renta declarado y los años de escolarización, y se diferencia el territorio según el tamaño demográfico del municipio. Las conclusiones se resumen de los modos siguientes. La mejora en el nivel educativo se observa generalizada, salvo en los municipios andaluces menores (≤2.000), envejecidos y despoblados, y en los andaluces y catalanes mayores (>100.000), repoblados mediante procesos de sustitución demográfica, evidentes en la Región Metropolitana de Barcelona y no en las mayores ciudades andaluzas. En cambio, se observa una relación, significativa en el caso catalán e importante en el andaluz, entre el crecimiento demográfico y el de los ingresos familiares. En Andalucía (durante los años observados), el aumento de renta se ha debido al de población y la merma de renta ha sido efecto de la pérdida de población en bastante medida. No ha ocurrido de igual forma o en la misma medida en Cataluña. Ésta es una fórmula insostenible, puesto que no se puede mejorar a costa de crecer indefinidamente. Cabe concluir, pues, que la homogeneidad territorial es indicador y efecto de la modernización, pero también una condición y un factor para su continuidad.

In this work we tackle the problem of the spam detection on the Web. Spam web pages have become a problem for Web search engines, due to the negative effects that this phe-nomenon can cause in their retrieval results. Our approach is based on a random-walk algorithm that obtains a ranking of pages according to their relevance and their spam likelihood. We introduce the novelty of taking into account the content of the web pages to characterize the web graph and to ob-tain an a- priori estimation of the spam likekihood of the web pages. Our graph-based algorithm computes two scores for each node in the graph. Intuitively, these values represent how bad or good (spam-like or not) is a web page, according to its textual content and the relations in the graph. Our experiments show that our proposed technique outperforms other link-based techniques for spam detection. Ministerio de Educación y Ciencia HUM2007-66607-C04-04

The automatic generation of user interfaces based on declarative models achieves a significant reduction of the development effort. In this paper, we analyze the feasibility of using two well-known techniques such as XInclude and Packaging in the new context of reusing user-interface model specifications. After analyzing the suitability of each technique for UI reutilization and implementing both techniques in a real system, we show that both techniques are suited to be used within the context of today’s existing model-based user interfaces.

In this work is described a collaborative tool Learning Activity Management System, LAMS (Macquarie University, Australia) which has been developed for designing, managing and delivering online collaborative learning activities. It provides teachers with a highly intuitive visual authoring environment for creating sequences of learning activities. These activities can include a range of individual tasks, small group work and whole class activities based on both content and collaboration. Then a methodology to apply this tool is described.

In this paper we investigate how to adapt the TextRank method to make it work in a supervised way. TextRank is a graph based method that applies the ideas of the ranking algorithm used in Google (PageRank) to Natural Language Processing (NLP) tasks. This approach has given very good results in many NLP tasks like text summarization, keyword extraction or word sense disambiguation. In all these tasks Text- Rank operates in an unsupervised way, without using any training corpus. Our main contribution is the definition of a method that allows to apply TextRank to a graph that includes information generated from a training tagged corpus. We have tested our method with the Part of Speech (POS) tagging task, comparing the results with those obtained with tools specialized in this task. The performance of our system is quite near to these tools, improving the results of two of them when the corpus tagset is big and therefore the tagging task more complicated. Ministerio de Ciencia y Tecnología TIN2004-07246-C03-03

In this paper we present an application of the stacking technique to a chunking task: named entity recognition. Stacking consists in applying machine learning techniques for combining the results of different models. Instead of using several corpus or several tagger generators to obtain the models needed in stacking, we have applied three transformations to a single training corpus and then we have used the four versions of the corpus to train a single tagger generator. Taking as baseline the results obtained with the original corpus (Fβ=1 value of 81.84), our experiments show that the three transformations improve this baseline (the best one reaches 84.51), and that applying stacking also improves this baseline reaching an Fβ=1 measure of 88.43.

In this paper we investigate the way of combining different taggers to improve their performance in the named entity recognition task. The main resources used in our experiments are the publicly available taggers TnT and TBL and a corpus of Spanish texts in which named entities occurrences are tagged with BIO tags. We have defined three transformations that provide us three additional versions of the training corpus. The transformations change either the words or the tags, and the three of them improve the results of TnT and TBL when they are trained with the original version of the corpus. With the four versions of the corpus and the two taggers, we have eight different models that can be combined with several techniques. The experiments carried out show that using machine learning techniques to combine them the performance improves considerably. We improve the baselines for TnT (Fβ=1 value of 85.25) and TBL (Fβ=1 value of 87.45) up to a value of 90.90 in the best of our experiments.

In this paper we investigate the way of improving the performance of a Named Entity Extraction (NEE) system by applying machine learning techniques and corpus transformation. The main resources used in our experiments are the publicly available tagger TnT and a corpus of Spanish texts in which named entities occurrences are tagged with BIO tags. We split the NEE task into two subtasks 1) Named Entity Recognition (NER) that involves the identification of the group of words that make up the name of an entity and 2) Named Entity Classification (NEC) that determines the category of a named entity. We have focused our work on the improvement of the NER task, generating four different taggers with the same training corpus and combining them using a stacking scheme. We improve the baseline of the NER task (Fβ=1 value of 81.84) up to a value of 88.37. When a NEC module is added to the NER system the performance of the whole NEE task is also improved. A value of 70.47 is achieved from a baseline of 66.07.

In this paper we show how an object-oriented specification language is useful for the specification of distributed systems. The main constructors in this language are the objects. An object consists of a state, a behaviour and a set of transition rules between states. The specification is composed of three sections: definition of algebraic data types to represent the domain of object attributes, definition of classes that group objects with common features, and definition of relationships among classes. We show two possible styles for defining the behaviour of objects, on one hand we use a transition system (state oriented) and on the other hand we use an algebraic model of processes description (constraint oriented). We illustrate the paper with the specification of the dining philosophers problem, a typical example in distributed programming. >

The association is a native concept from relational databases, one that has been adapted to object oriented (OO) modelling. It is an interesting operator used to describe links among objects of a system, commonly included in the most popular diagram-based OO methodologies. However, those methodologies sometimes present a lack of formality that may undermine its use. In this paper we formalize the semantics of associations. Firstly, we will describe an OO model based on different kinds of constraints. Some of them will be especially useful for describing the semantics of associations. Finally, we will present some remarks about implementation by means of triggers, a new feature incorporated in databases to specify an inner active behavior. Comisión Interministerial de Ciencia y Tecnología TIC97-0593-C05-03